Drought Patterns Research Articles

Spatiotemporal variability of precipitations and relationship with large-scale atmospheric indices in the Urubupungá Hydroelectric Cascade catchment area, Brazil

The proper isolation of climate effects over the water resources needs accurate knowledge of the precipitation regime. The Urubupungá Hydroelectric Cascade catchment area (UHC) overlaps more than half Itaipu Hydroelectric Plant catchment area in the Paraná River, in Brazil, which recently faced its first ever predicted water emergency. This study determines the spatiotemporal patterns of both droughts and excessive precipitations in the UHC catchment area with the principal component analysis (PCA) and constant wavelet analysis (CWT) and their global teleconnections with composite analysis and cross-wavelet analysis (XWT). The precipitation data used include 166 rain gauges with 42 years length (1976–2017) and were indexed by the Standardized Precipitation Index (SPI) during the dry season, the wet season and the water year. The rotation of Empirical Orthogonal Functions (EOF) with the varimax method revealed five sub-regions of temporal patterns in the study area. The dry season was the only that showed consistent EOF in all sub-regions, whose temporal pattern showed gradual and instant changes both concentrated northern and instant changes mostly in the 1990 decade. The composite analysis presented both positive and negative anomalies directly related with precipitations over two atmospheric indices, which are the Pacific Decadal Ocean and the Atlantic Multidecadal Ocean. The teleconnections of UHC with atmospheric indices of the Antarctic continent oscillations should be deeply investigated, since it had strong influence in XWT analysis but a slightly behavior in the composite analysis.

Climate Change and Gender: Mapping Drought and Gender Gap in Literacy and Employment Sections in Iran between 2011 and 2016.

Background: Drought is one of the most frequent natural hazards in Iran. Gender analysis can highlight the different needs and capacities of men and women to manage drought hazards. Thus, the present study aimed to map drought and the gender gap in drought data based on the provincial zones in 2011 and 2016. Methods: This cross-sectional study was conducted in 2 stages establishing a database and spatial analysis. Data mapping was done based on provincial divisions, sex-disaggregated distribution of literacy, and employment rate as well as drought patterns in Iran in 2011 and 2016 using ArcGIS software. Descriptive statistics were applied to analyze and report the sex-disaggregated literacy and employment data. Results: About 80.73% and 75.27% of women and 80.89% and 74.74% of men experienced severe and very severe droughts in 2011 and 2016, respectively. Gender inequality in the aspects of literacy and employment in drought-affected regions was found in 2011 and 2016. Conclusion: Community-based planning and management in regions exposed to climate change are suggested for reducing the consequences of climatic disasters such as droughts. Women need to be empowered and trained for innovative livelihood activities in rural and urban areas in Iran and other developing countries affected by long-term droughts.

Propagation from meteorological to hydrological drought and its application to drought prediction in the Xijiang River basin, South China

Exploring the propagation from meteorological drought to hydrological drought and its potential influence factors is crucial for early warning of hydrological drought. As a case study in the Xijiang River Basin (XRB), South China, the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) series from 1961 to 2013 were used to represent the meteorological drought and hydrological drought with daily runoff generated by VIC model. An identification process for the threshold and the mechanism of drought propagation was conducted based on rank order analysis and Copula theory. The drought propagation regulations of drought variables, geographical location, and lag-time were established by linear or non-linear functions. Eventually, the propagation regulations were applied to the hydrological drought prediction from 2021 to 2050. Results indicated that: (1) The spatial-temporal patterns of meteorological drought are the critical factors in triggering drought propagation, and the effects of meteorological drought area, drought severity, drought duration, drought spatial concentration, and drought persistent duration increase successively. The critical threshold for triggering drought propagation is the combined frequency of the above five drought variables greater than 0.14. (2) The geographic location, center number, duration, severity, and area of two drought types follow the linear, two-piecewise, power, linear and logarithmic functions, respectively. Drought mainly propagation from west to east in three-dimensional space, and the lag-time was less than three months, with the maximum being 78 days. Unlike triggering drought propagation, driving drought propagation is synthetically associated with meteorological, soil water, groundwater, and topography conditions. (3) Under the MRI-ESM2-0 model, the severity and area of hydrological drought under different scenarios in the 2021–2050 will increase compared with the reference period 1961–2013, and the extreme hydrological drought exceeding history may occur under scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. The findings can help policy-makers manage the early stages of hydrological drought based on meteorological drought conditions and thus reduce the negative impact of drought hazards.

Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa’s summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

Climatic fingerprint of spring discharge depletion in the southern Italian Apennines from 1601 to 2020 CE

Annual mean spring discharge (ASD) is an important water supply source, essential for ecological systems and societies dependent on groundwater resources. Influenced by both regional and local climate fluctuations, the inter-annual variability of ASD represents a climate memory signal, significantly affected when the drought pattern manifests itself in changing climatic regimes. Gaining a better historical perspective on ASD changes requires extended time-series of discharge data and relevant climate drivers. Here, using a parsimonious model, we present a continuous (modelled) time-series of annual ASD for the karst spring of Caposele, in the Cervialto Massif of southern Italy, which is hitherto the longest (1601–2020 CE) such time-series for the entire Mediterranean region. The model was designed to capture the importance of large-scale seasonal (spring, autumn and winter) precipitation (hydro-meteorological factor), and flood and drought indices (climatological factor), and to be consistent with a sample (1920–2020 CE) of actual data. We show a limited overall sensitivity of ASD to climate variability, with a mean of 4.21 m3 s−1 and a drop from ∼1759 CE. With a mean value of ∼3.60 m3 s−1 after ∼1987 CE, ASD has revealed a substantial descending trend—possibly a fingerprint of recent warming—with a depletion of regional water reservoirs. These results highlight the need to strengthen the capacity of groundwater resources in the face of changing, and possibly enhanced, drought patterns in the Mediterranean region.

Spatiotemporal drought analysis in Bangladesh using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI)

Countries depending on small-scale agriculture, such as Bangladesh, are susceptible to climate change and variability. Changes in the frequency and intensity of drought are a crucial aspect of this issue and the focus of this research. The goal of this work is to use SPI (standardized precipitation index) and SPEI (standardized precipitation evapotranspiration index) to investigate the differences in drought characteristics across different physiognomy types in Bangladesh and to highlight how drought characteristics change over time and spatial scales when considering different geomorphologies. This study used monthly precipitation and temperature data from 29 metrological stations for 39 years (1980–2018) for calculating SPI and SPEI values. To determine the significance of drought characteristic trends over different temporal and spatial scales, the modified Mann–Kendall trend test and multivariable linear regression (MLR) techniques were used. The results are as follows: (1) Overall, decreasing dry trend was found in Eastern hill regions, whereas an increasing drought trends were found in the in the rest of the regions in all time scaless (range is from − 0.08 decade−1 to − 0.15 decade−1 for 3-month time scale). However, except for the one-month time scale, the statistically significant trend was identified mostly in the north-central and northeast regions, indicating that drought patterns migrate from the northwest to the center region. (2) SPEI is anticipated to be better at capturing dry/wet cycles in more complex regions than SPI. (3) According to the MLR, longitude and maximum temperature can both influence precipitation. (4) Drought intensity increased gradually from the southern to the northern regions (1.26–1.56), and drought events occurred predominantly in the northwestern regions (27–30 times), indicating that drought meteorological hotspots were primarily concentrated in the Barind Tract and Tista River basin over time. Findings can be used to improve drought evaluation, hazard management, and application policymaking in Bangladesh. This has implications for agricultural catastrophe prevention and mitigation.

A Vulnerability Curve Method to Assess Risks of Climate-Related Hazards at County Level

A comprehensive risk assessment of different types of natural disasters at the county level can promote quantitative disaster risk assessment and can provide a scientific basis for the formulation of disaster prevention measures. Focusing on climate-related hazards and based on natural disaster risk assessment theories and methods, this study integrates disaster statistics, meteorological data, geographic information, and other multivariate data to quantify the hazards of various disasters and the vulnerability and exposure of hazard-bearing bodies and conducts an integrated assessment of comprehensive risks of multiple climate-related hazards in Cangnan County, Zhejiang Province. Typhoon disaster risk is high in the central and northern parts of this county and low in its surroundings, with high-risk areas mainly distributed in Lingxi Town to the north. The comprehensive risk distribution patterns of drought and flood disasters in Cangnan County are similar: low in the south and high in the north. With the method of standard deviation, the comprehensive risk of multiple climate-related hazards in Cangnan County shows a distribution pattern of being low in the south and high in the north, with high risk in the northeast and low risk in the northwest and south.

Changing Trends in Drought Patterns over the Northeastern United States Using Multiple Large Ensemble Datasets

Abstract Since the infamous extreme drought of the 1960s, the climate of the northeastern United States (NEUS) has generally trended toward warmer and wetter conditions. Nonetheless, there is mounting evidence that short-term droughts will continue to pose a significant risk for this region. To better explore the processes governing events such as these, climate models have adopted more complex representations of the fully coupled atmosphere–land–ocean–sea ice system; however, large uncertainties in future projections still persist, with internal variability necessitating large ensembles to understand trends in both rare and high-impact extreme events such as rapidly developing droughts (a term here that includes flash droughts developing on monthly scales). In this study, seven large ensemble (LE) models are employed to answer the outstanding question: How are the frequency and character of drought in the NEUS changing under a warming climate? We find that most LE models indicate the NEUS will experience a long-term wetting trend with more “extremely wet” months, but also more frequent short-term extreme droughts. These changes are associated with increasing precipitation, atmospheric water demand, and climate variability. We also conclude that discrepant trends in precipitation and evapotranspiration variability will lead to increasing anticorrelation of these variables, which is relevant to the intensification of rapidly developing drought, particularly in the spring season. These changes are associated with an increase in evapotranspiration from plants, brought by an earlier emergence of the growing season and denser vegetation. Significance Statement Droughts are extreme events with the potential to produce considerable social and economic damage. Because droughts emerge slowly and are relatively infrequent, large datasets are needed to study these features. Using multiple climate models, each producing multiple long-duration simulations, along with a novel drought index, we characterize rapidly developing droughts (those flash droughts identifiable from monthly data) and understand their drivers in the northeastern United States. We find that short-term extreme droughts are projected to be more frequent, while rapidly developing droughts will become more intense in the spring season. Intensification of rapidly developing droughts is attributed to differences in patterns of precipitation and evapotranspiration (soil evaporation and plant transpiration), which will be magnified by an extended growing season and an increase in vegetation.

A hydrologic perspective of major U.S. droughts

AbstractDrought is a recurring natural hazard that has substantial human and environmental impacts. Given continued global warming and associated climate change, there is concern that droughts could become more severe and longer lasting. To better monitor and understand drought development and persistence, it is helpful to understand the development and climatic drivers of past droughts. In this study we use monthly runoff percentiles to identify five major drought events in the conterminous United States (CONUS) from 1901 through 2020. For each drought event we examined spatial patterns of departures of mean monthly precipitation, temperature, soil moisture storage, and runoff for 2,107 hydrologic units (HUs) across the CONUS. Results indicated that precipitation deficits have been the primary driver of past major‐drought events and temperature a secondary driver, even of the most recent drought event (September 1999 through September 2015) when positive temperature anomalies occurred over most of the CONUS. Additionally, negative soil moisture storage departures were more negative than runoff departures during the five drought events we examined, which emphasizes the importance of measuring both runoff and soil moisture to monitor drought conditions. We also examined the use of statistical persistence to develop short‐term (i.e., 1 month) forecasts of runoff drought conditions in the CONUS by developing autoregressive integrated moving average (ARIMA) models for each HU. Results indicated that persistence can be used to predict short‐term changes in the spatial pattern of drought and the areal extent of drought, but that predictions of runoff magnitude for any particular site are often poor.

Multisource data-based integrated drought monitoring index: Model development and application

In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe.

Evaluation of TRMM satellite dataset for monitoring meteorological drought in northeastern Brazil

ABSTRACT Although droughts can inflict extreme damage on populations, few studies have characterized drought events in arid and semiarid regions around the globe due to the lack of reliable data. Therefore, precipitation estimates from remote sensing satellites have been increasingly used. In this study, the performance of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal patterns of drought at multiple time scales over Paraíba state is evaluated. The drought events are characterized by their quantity, duration, severity, intensity, frequency, and percentage of the area affected, and analyses are performed both for individual points and for four mesoregions at eight different time scales. The results indicate that short-term drought events occur more frequently but are less long-lasting and severe than long-term drought events. On the other hand, relevant changes in the average intensity of events cannot be identified among the time scales.

Temporal and Spatial Distribution Characteristics of Drought and Its Influence on Vegetation Change in Xilin Gol, China

Drought hinders economic and social growth in many areas of China, especially in livestock-dominated Xilin Gol League in Inner Mongolia. Most studies exclusively utilize rainfall to measure drought. To clarify the spatial and temporal distribution characteristics and evolution rules of meteorological drought, monthly observation data from nine meteorological stations in Xilin Gol were used to calculate the (effective drought index, EDI). We studied the spatiotemporal pattern of drought and its influence on vegetation in Xilin Gol using the Mann–Kendall test, (empirical orthogonal function, EOF) decomposition, and quantitative representation. (1) The annual average EDI declined by 0.029/10a, and Xilin Gol experienced an average of 0.5 drought occurrences every year. (2) A normal incidence in Xilin Gol is 67.17–72.65%, and that of severe drought is 0.02–0.99%. (3) Xilin Gol’s drought intensity is mostly concentrated in the central, northeast, and southwest regions, especially southwest and central. (4) The first two principal feature vectors in Xilin Gol contributed 52.75% and 14.38% to the variance. (5) The average (normalized differential vegetation index, NDVI )of desert, typical, and meadow steppe increased, especially in typical steppe (0.034/10a). (6) In Xilin Gol, the NDVI–EDI correlation coefficient ranges from −0.642 to 0.888, with an average of 0.392. Only 1.7% of the areas are adversely linked.

Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka

Developing an agricultural drought monitoring index through integrating multiple input variables into a single index is vital to facilitate the decision-making process. This study aims to develop an agricultural drought index (agCDI) to monitor and characterize the spatial and temporal patterns of drought in Sri Lanka. Long-term (1982 to 2020) remote sensing and model-based agroclimatic input parameters—normalized difference vegetation index (NDVI), land surface temperature (LST), 3-month precipitation z-score (stdPCP), and evaporative demand drought index (EDDI)—were used to develop agCDI. The principal component analysis (PCA) approach was employed to qualitatively determine the grid-based percentage contribution of each input parameter. The agCDI was apparently evaluated using an independent dataset, including the crop yield for the major crop growing districts and observed streamflow-based surface runoff index (SRI) for the two main crop growing seasons locally, called Yala (April to September) and Maha (October to March), using 20-years of data (from 2000 to 2020). The results illustrate the good performance of agCDI, in terms of predominantly capturing and characterizing the historic drought conditions in the main agricultural producing districts both during the Yala and Maha seasons. There is a relatively higher chance of the occurrence of moderate to extreme droughts in the Yala season, compared to the Maha season. The result further depicts that relatively good correlation coefficient values (> 0.6) were obtained when agCDI was evaluated using a rice crop yield in the selected districts. Although the agCDI correlated well with SRI in some of the stations (>0.6), its performance was somehow underestimated in some of the stations, perhaps due to the time lag of the streamflow response to drought. In general, agCDI showed its good performance in capturing the spatial and temporal patterns of the historic drought and, hence, the model can be used to develop agricultural drought monitoring and an early warning system to mitigate the adverse impacts of drought in Sri Lanka.

Modeling drought stress impacts under current and future climate for peanut in the semiarid pampas region of Argentina

In the semiarid pampas region of Argentina, peanut ( Arachis hypogaea L.) crop undergoes frequent and unpredictable drought stress periods, with high probability of occurrence under future climate change projections. However, the overall frequency of occurrence and timing of different drought stress patterns under current and future climates has not been well investigated for the main peanut region in central Argentina. The aims of this study were to: (i) define the main peanut drought stress patterns and their occurrence (frequency) during the peanut growing season, via a crop growth modeling approach, (ii) test seed yield stability of the formerly defined (in i) drought stress patterns under future climate scenarios, and (iii) analyze the effect of sowing dates on peanut seed yield as a management strategy to mitigate the impact of future climate scenarios for peanut seed yield in the semiarid pampas region of Argentina. The APSIM-peanut growth model was calibrated and validated for local genotypes and production environments. Our study simulated seasonal drought stress patterns at five representative locations across the region under current and future climate scenarios, and tested six sowing dates as a practice to mitigate the impact of climate change. Clustering analysis identified two contrasting environment types (ETs). The high stress ET showed greater frequency of occurrence (>50%) at the southern locations. Future climate conditions increased the frequency of high stress ET by roughly 6% and reduced peanut seed yield by 12%, as average across locations. For both current and future climates, earlier sowing dates maximized peanut seed yield, regardless the locations. Changing sowing date was not an effective practice to mitigate the negative impact of climate change on peanut seed yield. The defined ETs allowed identifying a target population of environments (TPE) having implications for optimizing peanut breeding and management strategies. The projected increase in the frequency of drought stress for all tested locations provides a challenging scenario for sustaining peanut productivity in this region. • The aims were to determine drought patterns under current and future climate, and to test sowing date as a mitigation tool • The defined drought stress patterns changed the frequency across locations, identifying a target population of environments • ·Future climate increased the frequency of drought stress by ~6% and reduced peanut seed yield by ~12% • Sowing date did not mitigate climate change impact. • This study provides insights to optimize breeding and management, and assists policy makers to address climate change.

Nitrogen and Phosphorus of Plants Associated with Arbuscular and Ectomycorrhizas Are Differentially Influenced by Drought.

Leaf nitrogen (N) and phosphorus (P) are the most important functional traits in plants which affect biogeochemical cycles. As the most widely observed plant–fungus mutualistic symbiosis, mycorrhiza plays a vital role in regulating plant growth. There are different types of mycorrhiza with various ecological functions in nature. Drought, as a frequent environmental stress, has been paid more and more attention due to its influence on plant growth. Numerous studies have confirmed that drought affects the concentration of N and P in plants, but few studies involve different mycorrhizal types of plants. In this study, the differences of N and P between arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) plants under different drought patterns, drought duration and cultivation conditions were explored based on a dataset by a meta-analysis. Drought stress (DS) showed negative effects on AM plant N (−7.15%) and AM plant P (−13.87%), and a positive effect on AM plant N:P ratio (+8.01%). Drought significantly increased N and the N:P ratio of ECM plants by 1.58% and 3.58%, respectively, and decreased P of ECM plants by −2.00%. Short-term drought (<30 d) reduces more N and P than long-term drought (<30 d) in AM plant species. The duration of drought did not change the N concentration of ECM plant N, while short-term drought reduced ECM plant P. The effects of N and P on DS also varied with different planting conditions and functional groups between AM and ECM plants. Therefore, mycorrhizal effects and stoichiometry of N and P play a key role in plant response to drought. So mycorrhizal effects should be considered when studying plant responses to drought stress.

Spatiotemporal Characteristics of Drought in Central Asia from 1981 to 2020

Drought is a meteorological phenomenon that threatens ecosystems, agricultural production, and living conditions. Central Asia is highly vulnerable to drought due to its special geographic location, water resource shortages, and extreme weather conditions, and poor management of water resources and reliance on irrigated agriculture exacerbate the effects of drought. In this study, the latest version of the Global Land Data Assimilation System was employed to calculate the Standardized Precipitation Evapotranspiration Index at different time scales during the period from 1981 to 2020. The varimax Rotated Empirical Orthogonal Function was applied for subregional delineation of drought patterns in Central Asia, and various methods were employed for a comparative analysis of the spatiotemporal characteristics of drought in these Central Asian subregions. The results show that drought patterns vary considerably in the Central Asian subregions. Over the past 40 years, alternating wet and dry conditions occurred in Central Asia. North Kazakhstan experienced more drought events with lower severity. East and west differences appear after 2001, the west becoming drier and the east becoming wetter. Some regions near lakes, such as Balkhash, Issyk-Kul, and the Aral Sea, suffer from droughts of long duration and high severity. In the Tianshan region, droughts in the northern slopes occur more frequently, with shorter durations and higher intensity and peaks. Northwestern China and western Mongolia have extensive agricultural land and grasslands with highly fragile ecosystems that have become progressively drier since 2001.

Understanding the impact of vegetation dynamics on the water cycle in the Noah-MP model

The impact of extreme climate events, especially prolonged drought, on ecosystem response, can influence the land-atmosphere interactions and modify local and regional weather and climate. To investigate the impact of vegetation dynamics on the simulation of energy, water, and carbon exchange at the land surface and streamflow, especially during drought conditions, we compared the performance of multiple versions of the Noah- multiparameterization (MP) land surface model (both Noah-MP LSM, version 3.6 and 4.0.1) with default configurations as well as various vegetation physics options, including the dynamic or input leaf area index (LAI) and the fractional vegetated area (FVEG). At the site level, simulated water and energy fluxes from each version were compared to eddy covariance (EC) flux tower measurements and remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) at well-characterized natural grassland sites in Kansas from 2008 to 2018. The ability of each version to reproduce annual mean river flows was compared to gauged observations at United States Geological Survey (USGS) stations over 11 years (2008–2018). Model performance in replicating spatial patterns during extreme events was assessed by comparing simulated soil moisture (SM) percentiles over the state of Kansas to the U.S. Drought Monitor (USDM). Results from these comparisons indicate that (a) even though there were differences in the latent heat (LE) components (i.e., transpiration, canopy evaporation, and soil evaporation), the total LE is mostly insensitive to variations in LAI across all model versions. This indicates that the incoming net radiation limits the total evaporation, as the presence of adequate soil moisture allows for higher soil evaporation when LAI limits transpiration; (b) regardless of the model version, the force of the precipitation largely dictates the accuracy of evapotranspiration (ET) simulation; (c) Overestimation of LE resulted in underestimation of streamflow, particularly over the land surface type dominated by a combination of grasses and cropland in the western and central part of the state; (d) all of the tested Noah-MP 4.0.1 vegetation physics produced spatial patterns of drought that more closely matched the USDM as compared to version 3.6. These findings have important relevance for applications of large-scale ecosystem-atmosphere feedbacks in water, carbon, and energy exchange.

Going Beyond Low Flows: Streamflow Drought Deficit and Duration Illuminate Distinct Spatiotemporal Drought Patterns and Trends in the U.S. During the Last Century

AbstractStreamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long‐term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period‐of‐record flows. We then analyzed drought trends using the Mann‐Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.

Spatiotemporal climate variability and meteorological drought characterization in Ethiopia

Increasing drought patterns with profound effects on livelihoods and food security have been documented in Ethiopia. From previous studies', assessments at various timescales, Ethiopia is regarded as a drought-prone country in East Africa. However, there is no documentation available. This paper investigates the spatiotemporal patterns of drought characteristics in 16 woredas (districts) as well as in the 14 homogeneous rainfall zones of homogeneous using monthly rainfall and temperature data over the period 1983 to 2020. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed using time-series meteorological data to depict the spatial extent of drought characteristics and patterns at 4- and 12-month timescales. Within the period investigated, 12-month, 2015, and 2019 recorded the most extreme countrywide drought. The most prolonged drought duration lasted for 12 months in 2015. Although Ethiopia is a drought-prone country, the frequency, magnitude, and severity vary spatially by region. In planning for future actions, particular emphasis must be paid to the northeastern, eastern, northwestern, and southeastern parts of the country, which are more vulnerable. The findings could potentially influence and redirect national drought management and disaster preparedness programs for the affected areas.

Use of Analytical Hierarchy Process (AHP) and Palmer Drought Severity Index (PDSI) to detect drought patterns (Dhi Qar-Iraq) study case

Use of Analytical Hierarchy Process (AHP) and Palmer Drought Severity Index (PDSI) to detect drought patterns (Dhi Qar-Iraq) study case